[Purpose/significance] Identifing the professional answerers with high probality in the social Q&A community can shorten the waiting time for users who ask questions to get satisfactory answers, promote knowledge sharing among users, and contribute to the sustainable and healthy development of the social Q&A community.[Method/process] Based on the social capital theory and motivation theory, this paper analyzed the motivation of users' answering questions, combined the expert discovery research to propose measurement indicators, and built a research model, then took Zhihu as a research example, and used Python to extract the eigenvalues and label of experimental data. Three common machine learning classification models, logistic regression model, random forest model and XGBoost model were used for training and prediction.[Result/conclusion] Compared with PageRank and HITS algorithms, the effectiveness and superiority of the method proposed by this paper have been verified. And this paper has provided a certain reference for the topic research of similar platforms such as healthy community problem push, expert identification and recommendation models.
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